quick.reg.temp.R

#' Regression Tables in Pixiedust
#'
#' Beautiful tables adding sums of squares
#' and p-value formatting, then giving html or
#' latex output. If interactive and HTML, will show up
#' in viewer.
#'
#' @param my.model Model. NOTE: If have factors, please place them first in the regression.
#' @param VIF include Variable Inflation Factor? (calculated by car::vif) (default=F)
#' @param part.eta If lm, include partial eta square by calculating SS_part/SS_total? (default=F)
#' @param myDF Dataframe, not needed if use data= in call
#' @param my.factor If there are any factors, list them here.
#' @param SS.type Type of sums of squares to report (default = 3)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param type Type of regression? Currently supported: lm, glm (binary), manova
#' @param do.glance Include glance stats?
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(my.model, myDF)

quick.reg.table = function(my.model,
                           part.eta = F,
                           VIF = F,
                           myDF = my.found.df,
                           SS.type = 2,
                           abbrev.length = ab.len,
                           pix.int = T,
                           pix.method = "html",
                           type = my.reg.type,
                           test.stat = "Pillai",
                           my.factor = NULL,
                           do.glance=T,
                           show.footer=T,
                           adjustment = "bonferroni",
                           show.contrasts=F,
                           show.y.contrasts=F,
                           show.latent=F,
                           show.intercepts=F,
                           real.names=T,
                           do.return=T) {
  library(pixiedust)
  library(broom)
  library(car)
  library(tidyr)
  library(phia)
  library(quick.tasks)
  library(dplyr)

  #### Find type ####
  my.reg.type=quick.type(my.model)

  #### Set Inits ####
  if(type=="ord"){
    ab.len=30
    library(ordinal)
  }else{
    ab.len=15
  }
  #### Get data frame from parent environment ####
  my.found.df = eval(parse(text=capture.output(my.model$call$data)),envir = parent.frame())
  #print(dim(my.found.df))
  if (is.null(my.found.df)) {
    stop(paste("No data frame found"))
  }




  UseMethod("quick.reg.table",my.model)
}

quick.reg.table.manova = function(my.model,
                                  part.eta = F,
                                  VIF = F,
                                  myDF = my.found.df,
                                  abbrev.length = ab.len,
                                  pix.int = T,
                                  pix.method = "html",
                                  type = my.reg.type,
                                  test.stat = "Pillai",
                                  my.factor = NULL,
                                  do.glance=T,
                                  show.footer=T,
                                  adjustment = "bonferroni",
                                  show.contrasts=F,
                                  show.y.contrasts=F,
                                  show.latent=F,
                                  show.intercepts=F,
                                  real.names=T,
                                  do.return=T,marginality=T) {


  #### for expansion
  if (!VIF & !part.eta) {
    v.p.len = 8
    v.p.rep = 0
  } else if (!VIF) {
    v.p.len = 9
    v.p.rep = 1
  } else if (!part.eta) {
    v.p.len = 9
    v.p.rep = 1
  } else{
    v.p.len = 10
    v.p.rep = 2
  }
  #### Begin MANOVA ####
  #### Inits ####
  my.y.levels=dim(my.model$model[[1]])[2]
  my.envir=environment()
  SS.type = 2
  my.nested.table=quick.SSCP(my.model, myDF, marginality, show.contrasts, show.latent,my.envir)
  #### Get treatment ####
  treat.model=my.nested.table[dim(my.nested.table)[1],4]

  #### Regular totals
  treat.total.temp=lapply(lapply(treat.model[[1]][[1]],diag),sum)
  treat.total=0
  for(W in 2:length(treat.total.temp)){
    treat.total=treat.total+treat.total.temp[[W]]
  }
  treat.total.df=sum(as.numeric(my.nested.table[,7]),na.rm = T)


  #### Partial totals
  part.treat.total=NULL
  for(X in 2:length(treat.model[[1]][[1]])){
    if(X==2){
      part.treat.total=treat.model[[1]][[1]][[X]]
    }else{
      part.treat.total=part.treat.total+treat.model[[1]][[1]][[X]]
    }
  }

  #### Latent totals
  if(show.latent){
    latent.treat.model=my.nested.table[dim(my.nested.table)[1],8]
    latent.part.treat.total=NULL
    latent.part.total=NULL
    for(X in 2:length(latent.treat.model[[1]][[1]])){
      if(X==2){
        latent.part.treat.total=latent.treat.model[[1]][[1]][[X]]
      }else{
        latent.part.treat.total=latent.part.treat.total+latent.treat.model[[1]][[1]][[X]]
      }
    }
  }



  #### Get residuals ####
  #### Regular residuals
  if(!marginality){
    the.resid=sum(diag(treat.model[[1]][[2]]))
    #### Partial residuals
    part.resid.total=treat.model[[1]][[2]]
  }else{
    the.resid=sum(diag(treat.model[[1]][[2]][[1]]))
    part.resid.total=treat.model[[1]][[2]][[1]]
  }
  the.resid.df=my.model$df.residual





  #### Latent totals
  if(show.latent){
    if(!marginality==3){
      latent.part.resid.total=latent.treat.model[[1]][[2]]
    }else{
      latent.part.resid.total=latent.treat.model[[1]][[2]][[1]]
    }
  }

  #### Get totals ####
  the.total=total.resid+treat.total+if(!marginality){sum(diag(treat.model[[1]][[1]][[1]]))}else{0}
  the.total.df=total.resid.df+treat.df+my.y.levels

  #### Partial totals
  partial.total=part.resid.total+part.treat.total+treat.model[[1]][[1]][[1]]

  #### Latent totals
  if(show.latent){
    latent.part.total=latent.part.treat.total+latent.part.treat.total+latent.treat.model[[1]][[1]][[1]]
  }



  #### Make dependent variable rownames ####
  my.dv.rownames=rownames(part.resid.total)

  #### Make basic table ####

  #### NEEDS TO BE FIXED ####
  my.table.names=c("var","test.stat","f.val","SS","df","mult.df","resid df","p.val")


  my.manova.table=as.data.frame(matrix(ncol=v.p.len,nrow=1))
  names(my.manova.table)=my.table.names
  my.line.var=1
  for(i in 1:length(my.SSP.treat)){
    if(i==1){
      my.i=i
    }else{
      my.i=2*i-1
    }
    #### Put in basic line ####
    my.treat.err=solve(my.SSP.err)%*%treat.model[[1]][[1]][[i]]
    my.test.stat=ifelse({i==1 & marginality},NA,quick.m.test(my.treat.err,test.stat))
    my.SS=ifelse({i==1 & marginality},0,sum(diag(treat.model[[1]][[1]][[i]])))
    my.df=my.y.levels*ifelse(my.i!=1,as.numeric(my.nested.table[my.i,7]),1)
    my.resid.df=ifelse(my.i==1,{my.y.levels*the.resid.df},min({the.resid.df*as.numeric(my.nested.table[my.i,5])-as.numeric(my.nested.table[my.i,5])},my.y.levels*the.resid.df-as.numeric(my.nested.table[my.i,5])))
    my.f.val=ifelse({i==1 & marginality},NA,{my.SS/my.df}/{the.resid/the.resid.df})
    my.p.val=ifelse({i==1 & marginality},NA,pf(my.f.val,my.df,my.resid.df,lower.tail = F))

    my.manova.table[my.line.var,]=c(ifelse(i==1,"(Intercept)",my.nested.table[my.i,1]),my.test.stat,my.f.val,my.SS,ifelse(my.i==1,NA,my.df/my.y.levels),ifelse(my.i==1,my.y.levels,my.df),my.resid.df,my.p.val)
    my.line.var=my.line.var+1


    #### Put in decomposed factors (y constrasts) ####
    #### Intercept
    if({show.intercepts & my.i==1}){
      for(y in 1:my.y.levels){
        my.name=my.dv.rownames[y]
        my.test.stat=NA
        my.SS=as.numeric(treat.model[[1]][[1]][[i]][y,y])
        my.df=1
        my.resid.df=the.resid.df
        my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
        my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)

        my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
        my.line.var=my.line.var+1
      }
    }
    #### Other
    if({show.y.contrasts & my.i!=1}){
      for(y in 1:my.y.levels){
        my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
        my.test.stat=NA
        my.SS=treat.model[[1]][[1]][[i]][y,y]
        my.df=as.numeric(my.nested.table[my.i,7])
        my.resid.df=min(abs(my.y.levels*the.resid.df-my.df*the.resid.df-my.df),abs(my.y.levels*the.resid.df-my.df))
        my.resid=part.resid.total[y,y]
        my.f.val={my.SS/my.df}/{my.resid/my.resid.df}
        my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)

        my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
        my.line.var=my.line.var+1


        #### Put in latents (ANOVA) ####
        my.counter=NULL
        for(r in 2:my.y.levels){
          my.counter=c(my.counter,r)
        }
        my.counter=c(my.counter,1)
        if(show.latent &my.i!=1){
          # my.my.i.temp=my.counter[my.i-1]
          my.my.i.temp=my.i
          my.y=y
          my.name=NA
          my.df=as.numeric(my.nested.table[my.i,7])
          my.test.stat=NA
          my.resid.df=the.resid.df-my.df
          my.SS=as.numeric(latent.treat.model[[1]][[1]][[i]][y,1])
          my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
          my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)

          my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
          my.line.var=my.line.var+1
        }

        #### Put in latent contrasts ####
        if(show.contrasts & show.latent & my.i!=1){
          #### Check length
          if({!is.na(my.nested.table[my.i,11])}){
            #other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
            for(k in 1:{my.nested.table[my.i,7]}){
              my.name=my.nested.table[my.i,11][[1]][k,1]
              my.f.val=as.numeric(my.nested.table[my.i,11][[1]][k,2])
              my.SS=as.numeric(my.nested.table[my.i,11][[1]][k,3])
              my.test.stat=NA
              my.df=1
              my.mult.df=NA
              my.resid.df=the.resid.df-my.y.levels+1
              my.p.val=as.numeric(my.nested.table[my.i,11][[1]][k,4])

              my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
              my.line.var=my.line.var+1
            }
          }
        }

        #### Put in contrasts ####
        #### Not right. Don't have it decomposed this way.
        if(show.contrasts & !show.latent & my.i!=1){
          #### Check length
          if(!is.na(my.nested.table[my.i,9])){
            #other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
            for(k in 1:as.numeric(my.nested.table[my.i,7])){
              my.name=my.nested.table[my.i,9][[1]][k,1]
              my.f.val=as.numeric(my.nested.table[my.i,9][[1]][k,2])
              my.SS=as.numeric(my.nested.table[my.i,9][[1]][k,3])
              my.test.stat=NA
              my.df=NA
              my.mult.df=my.y.levels
              my.resid.df=the.resid.df-my.y.levels+1
              my.p.val=as.numeric(my.nested.table[my.i,9][[1]][k,4])

              my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
              my.line.var=my.line.var+1
            }
          }
        }
      }
    }

    #### Put in contrasts ####
    if(show.contrasts & !show.latent & !show.y.contrasts){
      #### Check length
      if(!is.na(my.nested.table[my.i,8])){
        #other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
        for(k in 1:as.numeric(my.nested.table[my.i,7])){
          my.name=my.nested.table[my.i,8][[1]][k,1]
          my.f.val=as.numeric(my.nested.table[my.i,8][[1]][k,2])
          my.SS=as.numeric(my.nested.table[my.i,8][[1]][k,3])
          my.test.stat=NA
          my.df=NA
          my.mult.df=my.y.levels
          my.resid.df=the.resid.df-my.y.levels+1
          my.p.val=as.numeric(my.nested.table[my.i,8][[1]][k,4])

          my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
          my.line.var=my.line.var+1
        }
      }
    }


    #### Put in latents (ANOVA) ####
    my.counter=NULL
    for(r in 2:my.y.levels){
      my.counter=c(my.counter,r)
    }
    my.counter=c(my.counter,1)
    if(show.latent &my.i!=1 & !show.y.contrasts){
      #my.my.i.temp=my.counter[my.i-1]
      my.my.i.temp=my.i
      for(y in 1:{my.y.levels}){
        my.y=y
        my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
        my.df=as.numeric(my.nested.table[my.i,7])
        my.test.stat=NA
        my.resid.df=the.resid.df-my.df
        my.SS=as.numeric(my.nested.table[my.i,8][[1]][[1]][[i]][my.y])
        my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
        my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)

        my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
        my.line.var=my.line.var+1

        #### Put in latent contrasts ####
        if(show.contrasts & !{show.contrasts & show.latent}){
          #### Check length
          if({my.SSP.treat.df[my.i]>1}){
            other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
            for(k in 1:{my.SSP.treat.df[my.i]}){
              my.name=my.contrasts.table[[other.manova.grep]][k,1]
              my.f.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,2]
              my.SS=my.latent.contrasts.table[[other.manova.grep]][[y]][k,3]
              my.test.stat=NA
              my.df=1
              my.mult.df=NA
              my.resid.df=the.resid.df-my.y.levels+1
              my.p.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,4]

              my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
              my.line.var=my.line.var+1
            }
          }
        }
      }
    }


    #### Put in treatment ####
    #### From Type my.i statistics
    if(my.i==1){
      my.test.stat=quick.m.test(part.treat.total,test.stat)
      my.SS=as.numeric(treat.total)
      my.df=my.y.levels*as.numeric(treat.total.df)
      #### Should really be a min statement, but for later...
      my.resid.df=my.y.levels*as.numeric(the.resid.df)
      my.f.val={my.SS/my.df}/{as.numeric(the.resid.SS)/the.resid.df}
      my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)

      my.manova.table[my.line.var,]=c("Treatment Change",my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},my.df,my.resid.df,my.p.val)
      my.line.var=my.line.var+1

      #### Treatment Y Contrasts ####
      if(show.y.contrasts){
        for(b in 1:my.y.levels){
          my.test.stat=NA
          my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
          my.SS=latent.part.treat.total[b]
          my.df=the.resid.df
          #### Should really be a min statement, but for later...
          my.resid.df=my.y.levels*the.resid.df
          my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
          my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)

          my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},NA,{my.resid.df/my.y.levels},my.p.val)
          my.line.var=my.line.var+1

          #### Treatment Latents ####
          if(show.latent){
            #### Show latent treatments (ANOVAs)
            #### Need to change latents to type II
            my.SS=sum(weighted.residuals(my.null.model)[,b]^2)-sum(weighted.residuals(my.model)[,b]^2)
            my.df=as.numeric(treat.total.df)
            #### Should really be a min statement, but for later...
            my.resid.df=the.resid.df-my.df
            my.f.val={my.SS/my.df}/{my.latent.SSP.err[y,y]/my.resid.df}
            my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
            my.manova.table[my.line.var,]=c(NA,NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
            my.line.var=my.line.var+1
          }
        }
      }

      #### Latents ####
      my.counter=NULL
      for(r in 2:my.y.levels){
        my.counter=c(my.counter,r)
      }
      my.counter=c(my.counter,1)
      if(show.latent & !show.y.contrasts){
        for(b in 1:{my.y.levels}){
          my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
          my.SS=as.numeric(part.treat.total[b])
          my.df=as.numeric(treat.total.df)
          #### Should really be a min statement, but for later...
          my.resid.df=the.resid.df-my.df
          my.f.val={my.SS/my.df}/{as.numeric(latent.part.resid.total[b])/the.resid.df}
          my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
          my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
          my.line.var=my.line.var+1
        }
      }
    }
  }

  #### Put in residuals, total change, total ss ####
  my.manova.table[my.line.var,]=c("Total Change",NA,NA,as.numeric(treat.total),treat.total.df,{my.y.levels*treat.total.df},NA,NA,rep(NA,v.p.rep))
  my.line.var=my.line.var+1
  my.manova.table[my.line.var,]=c("Residuals",NA,NA,the.resid,the.resid.df,my.y.levels*the.resid.df,NA,NA,rep(NA,v.p.rep))
  my.line.var=my.line.var+1
  if(show.y.contrasts){
    for(i in 1:my.y.levels){
      my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,part.resid.total[i,i],the.resid.df,NA,NA,NA)
      my.line.var=my.line.var+1
      if(show.latent){
        my.manova.table[my.line.var,]=c(NA,NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
        my.line.var=my.line.var+1
      }
    }
  }
  if(show.latent & !show.y.contrasts){
    for(i in 1:my.y.levels){
      my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
      my.line.var=my.line.var+1
    }
  }
  my.manova.table[my.line.var,]=c("Total",NA,NA,the.total,the.total.change.df+1,my.y.levels*the.total.change.df+2,NA,NA,rep(NA,v.p.rep))
  if(show.footer){
    the.footer=paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")},if(show.latent){paste(" <br /> Latent Contrasts")})
  }else{
    the.footer=NULL
  }
  my.html.table=quick.table(my.manova.table,"manova",test=test.stat,marginality=marginality, abbrev.length = abbrev.length,the.footer = the.footer)
  if(do.return){
    return(my.html.table)
  }else{
    return()
  }
  #### ETA-SQ Stuff to finish.... ####
  # #### Make eta-sq
  # my.SSP.err.t=t(my.SSP.err)
  #
  # eta.sq=NULL
  # for(i in 1:length(my.SSP)){
  #   eta.sq[[i]]=my.SSP.err.t*my.SSP[[i]]
  # }
  #
  #       x3 = capture.output(car::Anova(my.model, type = SS.type, test = test.stat))
  #       my.manova.test = data.frame(matrix(ncol = 7, nrow = 1))
  #       my.var.temp = 4
  #       while (my.var.temp < {
  #         length(x3) - 1
  #       }) {
  #         test = strsplit(x3[my.var.temp], "\\s+")
  #
  #         if (length(test[[1]]) == 9) {
  #           test2 = test[[1]][-9]
  #           test2 = test2[-7]
  #
  #         } else if (length(test[[1]]) == 8) {
  #           test2 = test[[1]][-8]
  #
  #         } else{
  #           test2 = test[[1]]
  #         }
  #
  #         my.manova.test[{
  #           my.var.temp - 3
  #         }, ] = test2
  #         my.var.temp = my.var.temp + 1
  #
  #       }
  #       my.summary=summary(my.model)

  #### END MANOVA table ####
}



quick.reg.table.default = function(my.model,
                              part.eta = F,
                              VIF = F,
                              myDF = my.found.df,
                              marginality=T,
                              abbrev.length = ab.len,
                              pix.int = T,
                              pix.method = "html",
                              type = my.reg.type,
                              my.factor = NULL,
                              do.glance=T,
                              show.footer=T,
                              adjustment = "bonferroni",
                              show.contrasts=F,
                              show.intercepts=F,
                              do.return=T) {

  SS.type = 2

  if (type == "lm") {
    #### ANOVA TABLES ####
    my.summary = summary(my.model)
    my.coefficients = my.summary$coefficients
    my.coefficients = as.data.frame(my.coefficients)
    if (type == "ord" & length(my.model$model) == 1) {
      my.III.summary = NULL
      the.length = length(my.model$y.levels) - 1
    } else{
      if(marginality){
        my.III.summary = car::Anova(my.model, type = 2)
      }else{
        my.III.summary = car::Anova(my.model, type = 3)
      }


      if (type == "glm" & is.null(my.factor)) {
        the.length = dim(my.III.summary)[1] + 1
      } else{
        the.length = dim(my.III.summary)[1]
      }
    }

    #### Calculate total SS ####
    my.total = sum(my.III.summary$`Sum Sq`[2:length(my.III.summary$`Sum Sq`)])
    my.df.total = sum(my.III.summary$Df[2:length(my.III.summary$Df)])
    total.intercepts = 1
    my.rownames = c(abbreviate(rownames(my.summary$coefficients), minlength = abbrev.length),
                    "Residuals",
                    "Total")

    treat.SS=sum(my.III.summary$`Sum Sq`[2:{length(my.III.summary$`Sum Sq`)-1}])
    treat.df=sum(my.III.summary$Df[2:{length(my.III.summary$Df)-1}])

  }else if (type == "glm") {

    #### DevAn Table ####

    #### Inits ####
    new.df=my.model$model
    new.model=update(my.model,data=new.df)
    null.model=update(new.model,~1)

    total.intercepts = 1

    #### Check if null model ####
    vars.df=new.model$df.null-new.model$df.residual
    if(vars.df==0){
      stop("This is the null model.")
    }

    resid.dev=new.model$deviance
    resid.df=new.model$df.residual
    vars.dev=anova(new.model,test="Chi")$Deviance[-1]
    vars.dev.df=anova(new.model,test="Chi")$Df[-1]
    vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`[-1]
    total.dev=new.model$null.deviance-new.model$deviance
    vars.dev.total=sum(vars.dev)
    if(length(grep("*",new.model$formula))>0){
      my.full.model=new.model
    }else{
      my.vars=strsplit(as.character(new.model$formula),"~")
      my.dep.var=my.vars[[2]]
      my.other.vars=names(new.model$model)
      my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
      my.other.vars=my.other.vars[-my.var.grep]
      my.formula=paste("~",my.other.vars[1])
      for(i in 2:length(my.other.vars)){
        my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}

      #### FULL MODEL THING ####
      my.full.model=update(new.model,my.formula)
    }



    my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
    my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
    my.full.dev=sum(my.full.model.z)
    my.null.dev=sum(my.null.model.z)
    my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
    my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2

    treat.dev=anova(null.model,new.model)$`Deviance`[2]

    #vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
    #vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
    #total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
    total.dev.change=treat.dev+my.int.dev.total
    total.dev.change.df=total.intercepts*vars.df


    total.dev=new.model$null.deviance
    resid.dev=total.dev-total.dev.change

    vars.dev.df=anova(new.model,test="Chi")$Df
    resid.df=new.model$df.residual
    total.df=resid.df+vars.df




    my.int.dev.df=max({total.intercepts*sum(vars.dev.df,na.rm = T)-sum(vars.dev.df,na.rm=T)},1)
    my.int.dev.or=exp(coef(new.model))[1:{total.intercepts}]
    my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:total.intercepts,]
    my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)


    #resid.dev=new.model$deviance

    vars.dev=anova(new.model,test="Chi")$Deviance

    vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`
    vars.names=rownames(anova(new.model,test="Chi"))
    #total.dev=new.model$null.deviance-new.model$deviance
    vars.dev.total=sum(vars.dev)
    #total.df=sum(vars.dev.df,na.rm = T)

    vars.or=exp(coef(new.model))
    vars.or.confint=exp(confint(new.model,type="Wald"))
    my.names=abbreviate(names(vars.or),minlength=abbrev.length)
    #vars.or=exp(coef(new.model))[-1]
    #vars.or.confint=exp(confint(new.model,type="Wald"))[-1,]




  } else if (type == "ord" | type =="glm2") {
    new.df=my.model$model
    new.model=update(my.model,data=new.df)
    null.model=update(new.model,~1)

    total.intercepts = null.model$edf
    my.summary=summary(new.model)

    if(length(grep("*",new.model$formula))>0){
      my.full.model=new.model
    }else{
      my.vars=strsplit(as.character(new.model$formula),"~")
      my.dep.var=my.vars[[2]]
      my.other.vars=names(new.model$model)
      if(length(my.other.vars)>1){
        my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
        my.other.vars=my.other.vars[-my.var.grep]
        my.formula=paste("~",my.other.vars[1])
        for(i in 2:length(my.other.vars)){
          my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}

        #### FULL MODEL THING ####
        my.full.model=update(new.model,my.formula)
      }else{
        my.full.model=new.model
      }
    }

    my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
    my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
    my.full.dev=sum(my.full.model.z)
    my.null.dev=sum(my.null.model.z)
    my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
    my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2

    vars.df=new.model$edf-null.model$edf

    if(vars.df==0){
      stop("This is the null model.")
    }

    vars.dev=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],3]^2
    vars.dev.p=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],4]

    vars.dev.df=NULL
    weird.var=2
    track.var=1
    while(weird.var<=dim(new.model$model)[2]){
      if(is.factor(new.model$model[[weird.var]])){
        vars.dev.df[track.var]=length(levels(new.model$model[[weird.var]]))-1
      }else{
        vars.dev.df[track.var]=1
      }
      weird.var=weird.var+1
      track.var=track.var+1
    }

    ### interaction effects
    weird.var=weird.var+total.intercepts-1
    while(weird.var<=dim(summary(new.model)$coefficients)[1]){
      vars.dev.df[track.var]=1
      weird.var=weird.var+1
      track.var=track.var+1
    }

    vars.df.total=sum(vars.dev.df,na.rm = T)
    treat.dev=anova(null.model,new.model)$`LR.stat`[2]

    #vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
    #vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
    #total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
    total.dev.change=treat.dev+my.int.dev.total
    if(total.intercepts>1){
      total.dev.change.df=total.intercepts*vars.df.total
    }else{
      total.dev.change.df=vars.df.total+total.intercepts
    }

    resid.dev=-2*new.model$logLik
    total.dev=total.dev.change+resid.dev


    total.df=dim(myDF)[1]
    resid.df=total.df-total.dev.change.df

    vars.or=exp(coef(new.model))[{null.model$edf+1}:length(coef(new.model))]
    vars.or.confint=exp(confint(new.model,type="Wald"))[{null.model$edf+1}:length(coef(new.model)),]
    my.names=abbreviate(names(vars.or),minlength=abbrev.length)


    my.int.dev.df=max({total.intercepts*sum(vars.df,na.rm = T)-sum(vars.df,na.rm=T)},1)
    my.int.dev.or=exp(coef(new.model))[1:{null.model$edf}]
    my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:null.model$edf,]
    my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)


  } else{
    print("Error")
    return()
  }


  if (!VIF & !part.eta) {
    v.p.len = 7
    v.p.rep = 0
  } else if (!VIF) {
    v.p.len = 8
    v.p.rep = 1
  } else if (!part.eta) {
    v.p.len = 8
    v.p.rep = 1
  } else{
    v.p.len = 9
    v.p.rep = 2
  }

  #### Make table if not MANOVA ####
  my.tables.df = as.data.frame(matrix(ncol = v.p.len, nrow = 1))

  if (type == "lm") {
    if (!VIF & !part.eta) {
      my.std.error = c(my.coefficients$`Std. Error`, NA)
      my.estimate = c(my.coefficients$Estimate, NA)
      names(my.tables.df) = c("rownames",
                              "est",
                              "std.err",
                              "f.val",
                              "sum.sq",
                              "df",
                              "p.val")
    } else if (!part.eta) {
      my.VIF = car::vif(my.model)
      names(my.tables.df) = c("rownames",
                              "sumsq",
                              "df",
                              "est",
                              "std.err",
                              "f.val",
                              "p.val",
                              "VIF")
    } else if (!VIF) {
      names(my.tables.df) = c("rownames",
                              "sumsq",
                              "df",
                              "est",
                              "std.err",
                              "f.val",
                              "p.val",
                              "p.eta")
    } else{
      my.VIF = car::vif(my.model)
      names(my.tables.df) = c("rownames",
                              "sumsq",
                              "df",
                              "est",
                              "std.err",
                              "f.val",
                              "p.val",
                              "p.eta",
                              "VIF")
    }
  } else if (type == "glm" & VIF) {
    my.VIF = car::vif(my.model)
    names(my.tables.df) = c("var",
                            "p.odd",
                            "p.odd.2.5",
                            "p.odd.97.5",
                            "deviance",
                            "df",
                            "p.val",
                            "VIF")
  } else{
    names(my.tables.df) = c("var", "p.odd", "p.odd.2.5", "p.odd.97.5", "deviance", "df", "p.val")
  }

  #### Make the double table entries ####

  #### Was very annoying...took my frustration out on
  #### Variable names

  factor.stupid = NULL
  factor.rownames = NULL
  num.of.levels = NULL
  ord.temp = 0
  if (!is.null(my.factor)) {
    for (i in 1:length(my.factor)) {
      factor.stupid = c(factor.stupid, grep(paste("^", my.factor[i], "$", sep =
                                                    ""), names(myDF)))

      if (type == "lm") {
        factor.rownames = c(factor.rownames, grep(
          paste("^", my.factor[i], "$", sep = ""),
          rownames(my.III.summary)
        ))
        num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
        # }else if(type=="glm"){
        #   factor.rownames=c(factor.rownames,{grep(paste("^",my.factor[i],"$",sep=""),rownames(my.III.summary))+1})
        #   num.of.levels=c(num.of.levels,length(levels(myDF[[factor.stupid[i]]])))
      } else{
        my.dev.grep=grep(paste("^", my.factor[i], sep = ""),names(new.model$coefficients))[1]
        factor.rownames=c(factor.rownames,my.dev.grep)
        num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))

        # factor.rownames = c(factor.rownames, {
        #   grep(paste("^", my.factor[i], "$", sep = ""),
        #        rownames(my.III.summary)) + total.intercepts + sum(num.of.levels) - ordinal.temp
        # })
        # num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
        # ordinal.temp = ordinal.temp + 1
      }
    }

  } else{
    factor.rownames = 0L

  }



  #### Make phia stuff ####
  if(show.contrasts){
    if(type=="glm" & !is.null(my.factor)){
      my.phia.reg=quick.contrast(new.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length,my.factors = my.factor)
      my.phia.rownames=my.phia.reg$names
      phia.dev=my.phia.reg$Chisq

    }else if(type=="lm" & !is.null(my.factor)){
      my.phia.reg=quick.contrast(my.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length)
      my.j=0
      my.big.phia=NULL
      phia.shift=0
      real.shift=0
      for(i in 1:dim(my.phia.reg)[1]){
        if(!is.na(my.phia.reg[i,1])){
          my.j=my.j+1
          real.shift=real.shift+phia.shift
          my.big.phia[[my.j]]=my.phia.reg[i,2:7]
        }else{
          my.big.phia[[my.j]][i-real.shift,]=my.phia.reg[i,2:7]
        }
        phia.shift=phia.shift+1
      }

      my.phia.rownames=NULL
      my.phia.SS=NULL
      my.phia.value=NULL
      my.phia.F=NULL
      my.phia.p=NULL
      my.phia.err=NULL
      for(i in 1:{my.j}){
        my.phia.rownames[[i]]=my.big.phia[[i]]$names
        my.phia.SS[[i]]=my.big.phia[[i]]$`Sum of Sq`
        my.phia.value[[i]]=my.big.phia[[i]]$Value
        my.phia.F[[i]]=my.big.phia[[i]][,5]
        my.phia.p[[i]]=my.big.phia[[i]][,6]

      }
    }else{

    }
  }



  my.factor.var = 1
  this.temp.var = 1
  this.shift.temp = 1
  yet.another.var = 1
  my.shift = 0
  other.temp = 2
  ord.temp = 0
  phia.temp=1
  if (type == "lm") {
    dang.length = length(rownames(my.III.summary))
  } else if (type == "glm") {
    # dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
    #   sum(num.of.levels) - length(my.factor)
    # }, 0) + 1
    dang.length=length(new.model$coefficients)-sum(num.of.levels)+length(num.of.levels)+total.intercepts+1
  } else{
    # dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
    #   sum(num.of.levels) - length(my.factor)
    # }, 0) + 1
    #dang.length=7
    dang.length=length(new.model$coefficients)+length(my.factor)+1
  }



  while (this.shift.temp < dang.length) {
    if (is.na(factor.rownames[my.factor.var])) {
      my.factor.rownames = 1

    } else{
      my.factor.rownames = factor.rownames[my.factor.var]

    }
    #### LOOP ####
    if (this.shift.temp == 1) {
      i = 1
      if(type=="ord" | type=="glm"){
        my.tables.df[this.temp.var, ] = c(
          "Intercept Change",
          NA,
          NA,
          NA,
          my.int.dev.total,
          my.int.dev.df,
          pchisq(my.int.dev.total,my.int.dev.df,lower.tail = F),
          rep(NA, v.p.rep))

        this.temp.var=this.temp.var+1
      }
      while (i <= total.intercepts) {
        if (type == "lm") {
          my.sumsq = ifelse(marginality,NA,my.III.summary$`Sum Sq`[this.shift.temp])
          my.df = my.III.summary$Df[this.shift.temp]
          my.est = ifelse(marginality,0,my.estimate[this.shift.temp])
          my.std.err = ifelse(marginality,NA,my.std.error[this.shift.temp])
          my.f.val = ifelse(marginality,NA,my.III.summary$`F value`[this.shift.temp])
          my.p.val = ifelse(marginality,NA,my.III.summary$`Pr(>F)`[this.shift.temp])
          my.tables.df[this.temp.var, ] = c(
            my.rownames[this.shift.temp],
            my.est,
            my.std.err,
            my.f.val,
            my.sumsq,
            my.df,
            my.p.val,
            rep(NA, v.p.rep)
          )
        } else if(type=="glm"){
          #### HERE IS WHERE I LEFT OFF!! ####
          # my.or = exp(my.estimate[this.shift.temp])
          # my.est = my.estimate[this.shift.temp]
          # my.z.val = my.summary$coefficients[this.shift.temp, 3]
          # my.std.err = my.std.error[this.shift.temp]
          #my.dev=my.III.summary$`LR Chisq`[this.shift.temp]
          #my.df=my.III.summary$Df[this.shift.temp]
          # my.p.val = my.summary$coefficients[{
          #   this.shift.temp
          #}, 4]

          my.tables.df[this.temp.var, ] = c(
            paste(names(attr(my.model$model[[i]],"labels"))[1],"-",names(attr(my.model$model[[i]],"labels"))[2],sep=""),
            my.int.dev.or[i],
            my.int.dev.or.confint[1],
            my.int.dev.or.confint[2],
            my.int.dev[i],
            1,
            NA,
            rep(NA, v.p.rep))

          #this.temp.var=this.temp.var+1
          #my.tables.df[this.temp.var,]=c("Treatment",NA,NA,NA,total.dev,total.df,dchisq(total.dev,total.df),rep(NA,v.p.rep))

        }else{
          my.tables.df[this.temp.var, ] = c(
            my.int.names[i],
            my.int.dev.or[i],
            my.int.dev.or.confint[i,1],
            my.int.dev.or.confint[i,2],
            my.int.dev[i],
            NA,
            NA,
            rep(NA, v.p.rep))

          #this.temp.var=this.temp.var+1

        }

        this.shift.temp = this.shift.temp + 1

        this.temp.var = this.temp.var + 1
        i = i + 1

      }
      if(type=="ord" | type=="glm"){
        my.tables.df[this.temp.var,]=c("Treatment Change",NA,NA,NA,treat.dev,vars.df,pchisq(treat.dev,vars.df,lower.tail = F))
        this.temp.var=this.temp.var+1
      }else{
        my.tables.df[this.temp.var,]=c("Treatment Change",NA,NA,{treat.SS/treat.df}/{my.III.summary$`Sum Sq`[nrow(my.III.summary$`Sum Sq`)]/my.III.summary$Df[nrow(my.III.summary$Df)]},treat.SS,treat.df,pf({{treat.SS/treat.df}/{my.III.summary$`Sum Sq`[nrow(my.III.summary$`Sum Sq`)]/my.III.summary$Df[nrow(my.III.summary$Df)]}},treat.df,my.III.summary$Df[nrow(my.III.summary$Df)],lower.tail = F),rep(NA,v.p.rep))
        this.temp.var=this.temp.var+1
      }
    } else if (this.shift.temp == my.factor.rownames) {
      if (type == "lm") {
        my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
        my.df = my.III.summary$Df[this.shift.temp]
        my.est = my.estimate[this.shift.temp]
        my.std.err = my.std.error[this.shift.temp]
        my.z.val = NA
        my.f.val = my.III.summary$`F value`[this.shift.temp]
        my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
        if(!VIF & !part.eta){
          my.tables.df[this.temp.var, ] = c(
            my.factor[yet.another.var],
            NA,
            NA,
            my.f.val,
            my.sumsq,
            my.df,
            my.p.val,
            rep(NA, v.p.rep)
          )
        }else if(part.eta & !VIF){
          my.p.eta = my.sumsq / my.total
          my.tables.df[this.temp.var, ] = c(
            my.factor[yet.another.var],
            NA,
            NA,
            my.f.val,
            my.sumsq,
            my.df,
            my.p.val,
            my.p.eta
          )
        }else if(!part.eta & VIF){

          my.tables.df[this.temp.var, ] = c(
            my.factor[yet.another.var],
            NA,
            NA,
            my.f.val,
            my.sumsq,
            my.df,
            my.p.val,
            my.VIF[this.shift.temp - 1, 1]
          )
        }else{
          my.p.eta = my.sumsq / my.total
          my.tables.df[this.temp.var, ] = c(
            my.factor[yet.another.var],
            NA,
            NA,
            my.f.val,
            my.sumsq,
            my.df,
            my.p.val,
            my.p.eta,
            my.VIF[this.shift.temp - 1, 1]
          )
        }
      }else{

        my.tables.df[this.temp.var,]=c(my.factor[this.shift.temp-1],
                                       NA,
                                       NA,
                                       NA,
                                       drop1(new.model,test="Chi")$`LRT`[this.shift.temp],
                                       drop1(new.model,test="Chi")$`Df`[this.shift.temp],
                                       drop1(new.model,test="Chi")$`Pr(>Chi)`[this.shift.temp],
                                       rep(NA,v.p.rep))

      }


      yet.another.var = yet.another.var + 1
      this.temp.var = this.temp.var + 1
      this.shift.temp = this.shift.temp + 1
      if(type=="lm"){
        other.other.temp = 2
      }else{
        other.other.temp=1
      }

      #### INTERACTION EFFECTS? NOT WORRIED YET ####
      if ({length(grepl(":", my.summary$coefficients[other.temp, 1])) >
          0}) {

        #### I think the while should not be +1
        if(type=="glm" | type=="ord"){
          num.of.levels[my.factor.var]=num.of.levels[my.factor.var]-1
        }
        while (other.other.temp < {
          num.of.levels[my.factor.var] + 1
        }) {
          if (type == "lm") {
            if(show.contrasts){
              my.sumsq = NA
              my.df = NA
              my.est = my.estimate[other.temp]
              my.std.err = my.std.error[other.temp]
              #### NEED TO FIX ####
              my.f.val = {
                my.summary$coefficients[other.temp, 3] ^ 2
              }
              my.p.val = my.summary$coefficients[other.temp, 4]
              my.tables.df[this.temp.var, ] = c(
                my.phia.rownames[[phia.temp]][other.temp-1],
                my.phia.value[[phia.temp]][other.temp-1],
                my.est,
                my.phia.SS[[phia.temp]][other.temp-1],
                1,
                my.phia.F[[phia.temp]][other.temp-1],
                my.phia.p[[phia.temp]][other.temp-1],
                rep(NA, v.p.rep)
              )
            }
            ord.temp=ord.temp + 1
          } else if (type == "glm") {
            # my.or = exp(my.estimate[other.temp])
            # my.est = my.estimate[other.temp]
            # my.std.err = my.std.error[other.temp]
            # my.z.val = my.summary$coefficients[other.temp, 3]
            # my.dev = my.III.summary$`LR Chisq`[other.temp]
            # my.df = my.III.summary$Df[other.temp]
            # my.p.val = my.summary$coefficients[other.temp, 4]
            # my.tables.df[this.temp.var, ] = c(my.rownames[other.temp],
            #                                   my.or,
            #                                   my.std.err,
            #                                   my.z.val,
            #                                   NA,
            #                                   NA,
            #                                   my.p.val)
            if(show.contrasts){
              my.tables.df[this.temp.var, ] = c(my.phia.rownames[other.temp-1],
                                                vars.or[other.temp-1],
                                                vars.or.confint[other.temp-1,1],
                                                vars.or.confint[other.temp-1,2],
                                                my.phia[other.temp-1,3],
                                                my.phia[other.temp-1,4],
                                                my.phia[other.temp-1,6],rep(NA,v.p.rep))
              #this.shift.temp = this.shift.temp + 1
            }
            ord.temp=ord.temp + 1
          } else{
            my.or = exp(my.estimate[other.temp + total.intercepts - 1])
            my.est = my.estimate[other.temp + total.intercepts - 1]
            my.std.err = my.std.error[other.temp + total.intercepts -
                                        1]
            my.z.val = my.summary$coefficients[{
              other.temp + total.intercepts - 1
            }, 3]
            #my.dev=my.III.summary$Chisq[other.temp]
            #my.df=my.III.summary$Df[other.temp]
            my.p.val = my.summary$coefficients[{
              other.temp + total.intercepts - 1
            }, 4]
            if (!VIF) {
              my.tables.df[this.temp.var, ] = c(my.rownames[other.temp +
                                                              total.intercepts - 1],
                                                my.or,
                                                my.std.err,
                                                my.z.val,
                                                NA,
                                                NA,
                                                my.p.val)
            } else{
              my.tables.df[this.temp.var, ] = c(my.rownames[other.temp +
                                                              total.intercepts - 1],
                                                my.or,
                                                my.std.err,
                                                my.z.val,
                                                NA,
                                                NA,
                                                my.p.val,
                                                my.VIF[this.shift.temp - 1, 1])

            }
            this.shift.temp = this.shift.temp + 1
          }
          this.temp.var = this.temp.var + 1
          other.temp = other.temp + 1
          other.other.temp = other.other.temp + 1
          the.length = the.length + 1

        }
        phia.temp=phia.temp+1
        if(!show.contrasts){this.temp.var=this.temp.var-ord.temp}
      } else{

      }

      if (my.factor.var == 1) {
        my.shift = {
          my.shift + other.temp - 2
        }

      } else{
        my.shift = my.shift + other.temp

      }

      my.factor.var = my.factor.var + 1

    } else{
      if (type == "lm") {
        my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
        my.df = my.III.summary$Df[this.shift.temp]
        my.est = my.estimate[this.shift.temp]
        my.std.err = my.std.error[this.shift.temp]
        my.f.val = my.III.summary$`F value`[this.shift.temp]
        my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
        if (!VIF & !part.eta) {
          my.tables.df[this.temp.var, ] = c(
            rownames(my.III.summary)[this.shift.temp],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
            my.sumsq,
            my.df,
            my.f.val,
            my.p.val
          )
        } else if (!part.eta) {
          my.tables.df[this.temp.var, ] = c(
            rownames(my.III.summary)[this.shift.temp],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
            my.sumsq,
            my.df,
            my.f.val,
            my.p.val,
            my.VIF[this.shift.temp - 1, 1]
          )
        } else if (!VIF) {
          my.p.eta = my.sumsq / my.total
          my.tables.df[this.temp.var, ] = c(
            rownames(my.III.summary)[this.shift.temp],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
            my.sumsq,
            my.df,
            my.f.val,
            my.p.val,
            my.p.eta
          )
        } else{
          my.p.eta = my.sumsq / my.total
          my.tables.df[this.temp.var, ] = c(
            rownames(my.III.summary)[this.shift.temp],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
            summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
            my.sumsq,
            my.df,
            my.f.val,
            my.p.val,
            my.p.eta,
            my.VIF[this.shift.temp - 1, 1]
          )
        }
        ord.temp=ord.temp+1
      } else if (type == "glm") {
        # if (!is.null(my.factor)) {
        #   this.shift.temp = this.shift.temp - ord.temp-1
        # }
        # my.or = exp(my.estimate[this.shift.temp])
        # my.est = my.estimate[this.shift.temp]
        # my.std.err = my.std.error[this.shift.temp]
        # my.z.val = my.summary$coefficients[other.temp, 3]
        # my.dev = my.III.summary$`LR Chisq`[ord.temp]
        # my.df = my.III.summary$Df[ord.temp]
        # my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
        if (!VIF) {
          # my.tables.df[this.temp.var, ] = c(my.rownames[this.shift.temp],
          #                                   my.or,
          #                                   my.std.err,
          #                                   NA,
          #                                   my.dev,
          #                                   my.df,
          #                                   my.p.val)
          my.tables.df[this.temp.var,]=c(names(vars.or)[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
                                         vars.or[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
                                         vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},1],
                                         vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},2],
                                         vars.dev[this.shift.temp],
                                         vars.dev.df[this.shift.temp],
                                         vars.dev.p[this.shift.temp],rep(NA,v.p.len))
        } else{
          my.tables.df[this.temp.var, ] = c(names(vars.or)[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
                                            vars.or[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
                                            vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},1],
                                            vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},2],
                                            vars.dev[this.shift.temp],
                                            vars.dev.df[this.shift.temp],
                                            vars.dev.p[this.shift.temp],
                                            rep(NA,v.p.len))
        }
        # if (!is.null(my.factor)) {
        #   this.shift.temp = this.shift.temp + ord.temp+1
        # }
        yet.another.var=yet.another.var+1
        ord.temp = ord.temp + 1
      } else{
        if (!is.null(my.factor)) {
          this.shift.temp = this.shift.temp - ord.temp
        }else{
          this.shift.temp=this.shift.temp-total.intercepts
        }
        my.tables.df[this.temp.var, ] = c(names(vars.or)[this.shift.temp],
                                          vars.or[{this.shift.temp}],
                                          if(!is.null(dim(vars.or.confint))){
                                            vars.or.confint[{this.shift.temp},1]
                                          }else{
                                            vars.or.confint[1]
                                          },
                                          if(!is.null(dim(vars.or.confint))){
                                            vars.or.confint[{this.shift.temp},2]
                                          }else{
                                            vars.or.confint[2]
                                          },
                                          vars.dev[this.shift.temp-ord.temp],
                                          vars.dev.df[this.shift.temp-ord.temp],
                                          vars.dev.p[this.shift.temp-ord.temp],
                                          rep(NA,v.p.len))
        if (!is.null(my.factor)) {
          this.shift.temp = this.shift.temp + ord.temp
        }else{
          this.shift.temp=this.shift.temp+total.intercepts
        }
        #ord.temp = ord.temp + 1
      }
      this.shift.temp = this.shift.temp + 1
      this.temp.var = this.temp.var + 1

    }
  }

  if (type == "lm") {
    my.tables.df[this.temp.var, ] = c("Total Change",
                                      NA,
                                      NA,
                                      treat.SS,
                                      treat.df,
                                      rep(NA, v.p.rep + 2))
    my.tables.df[this.temp.var + 1, ] = c(
      "Residuals",
      NA,
      NA,
      my.III.summary$`Sum Sq`[this.shift.temp],
      my.III.summary$Df[this.shift.temp],
      NA,
      NA,
      rep(NA, v.p.rep)
    )
    my.tables.df[this.temp.var+2,]=c("Total",NA,NA,my.total+my.III.summary$`Sum Sq`[1],{treat.df+my.III.summary$Df[this.shift.temp]},rep(NA,v.p.rep+2))
    if (!VIF & !part.eta) {

    } else if (!VIF) {
      my.tables.df$p.eta = as.numeric(my.tables.df$p.eta)
    } else if (!part.eta) {
      my.tables.df$VIF = as.numeric(my.tables.df$VIF)
    } else{
      my.tables.df$p.eta = as.numeric(my.tables.df$p.eta)
      my.tables.df$VIF = as.numeric(my.tables.df$VIF)
    }
    my.tables.df$f.val = as.numeric(my.tables.df$f.val)
    my.tables.df$est = as.numeric(my.tables.df$est)
    my.tables.df$sumsq = as.numeric(my.tables.df$sumsq)
    my.tables.df$df = as.numeric(my.tables.df$df)
    my.tables.df$std.err = as.numeric(my.tables.df$std.err)
    my.tables.df$p.val = as.numeric(my.tables.df$p.val)
  } else{

    my.tables.df[this.temp.var,]=c("Total Change",NA,NA,NA,total.dev.change,total.dev.change.df,pchisq(total.dev.change,total.dev.change.df,lower.tail = F),rep(NA,v.p.rep))
    my.tables.df[this.temp.var+1,]=c("Residuals",NA,NA,NA,resid.dev,resid.df,NA,rep(NA,v.p.rep))
    my.tables.df[this.temp.var+2,]=c("Total",NA,NA,NA,total.dev,total.df,NA,rep(NA,v.p.rep))


    #my.tables.df[this.temp.var,] = c("Change from Null", NA, NA, NA, ddeviance2, ddf2, fit2)
    my.tables.df$p.odd = as.numeric(my.tables.df$p.odd)
    my.tables.df$p.odd.2.5 = as.numeric(my.tables.df$p.odd.2.5)
    my.tables.df$p.odd.97.5 = as.numeric(my.tables.df$p.odd.97.5)
    my.tables.df$deviance=as.numeric(my.tables.df$deviance)
    my.tables.df$p.val=as.numeric(my.tables.df$p.val)
    if (VIF) {
      my.tables.df$VIF = as.numeric(my.tables.df$VIF)
    }
  }



  #### Make custom glance stats ####
  if(do.glance){
    #### Can eventually make it options
    if (type == "lm") {
      glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
      glance_stats[1,]=c(paste("Method: ","QR Decomposition",if(show.contrasts){paste("<br />Adjustment Method: ",adjustment,sep="")},sep=""),rep(NA,6),rep(NA,v.p.rep))

      # glance_stats = broom::glance(my.model)
      # glance_stats = tidyr::gather(glance_stats)
      #
      #
      # glance_stats[3:{
      #   5 + v.p.rep
      # }] = NA
      # glance_stats[6 + v.p.rep] = c(glance_stats$key[7:8],
      #                               NA,
      #                               glance_stats$key[9:11],
      #                               NA,
      #                               NA,
      #                               NA,
      #                               NA,
      #                               NA)
      # glance_stats[7 + v.p.rep] = c(glance_stats$value[7:8],
      #                               NA,
      #                               glance_stats$value[9:11],
      #                               NA,
      #                               NA,
      #                               NA,
      #                               NA,
      #                               NA)
      # glance_stats = glance_stats[-7:-11, ]
      # glance_stats = glance_stats[-3, ]
      # glance_stats = glance_stats[-5, ]

    } else if (type == "glm") {
      glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
      glance_stats[1,]=c(paste("Family: ",new.model$family$family," <br /> Link: ",new.model$family$link,if(show.contrasts){paste(" <br />Adjustment: ",adjustment,sep="")},sep=""),rep(NA,6),rep(NA,v.p.rep))

      # glance_stats = broom::glance(my.model)
      # glance_stats = tidyr::gather(glance_stats)
      #
      #
      # glance_stats[[3]] = c("Family: ", "Link: ", rep(NA, 5))
      # glance_stats[[4]] = c(my.model$family$family,
      #                       my.model$family$link,
      #                       rep(NA, 5))
      # glance_stats[5:{
      #   5 + v.p.rep
      # }] = NA
      # glance_stats[6 + v.p.rep] = c(glance_stats$key[3:6], NA, NA, NA)
      # glance_stats[7 + v.p.rep] = c(glance_stats$value[3:6], NA, NA, NA)
      # glance_stats[[1]] = c("Null.dev", "Chi-Sq", "dF", "Pr(>Chisq)", NA, NA, NA)
      # glance_stats[[2]] = c(
      #   glance_stats$value[1],
      #   ddeviance2,
      #   ddf2,
      #   pvalString(fit2, digits = 3, format = "default"),
      #   NA,
      #   NA,
      #   NA
      # )
      # glance_stats = glance_stats[-5:-7, ]
      # glance_stats2=as.data.frame(matrix(ncol=7,nrow=1))
      # glance_stats2[1,]=c(glance_stats$key[1],glance_stats$value[1],NA,NA,NA,glance_stats$key[6],glance_stats$value[6])
      # glance_stats2[2,]=c(glance_stats$key[2],glance_stats$value[2],NA,NA,NA,glance_stats$key[7],glance_stats$value[7])
      # glance_stats2[3,]=c(glance_stats$key[3],glance_stats$value[3],NA,NA,NA,glance_stats$key[4],glance_stats$value[4])
      # glance_stats=glance_stats2
    } else if(type=="ord"){
      glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
      glance_stats[1,]=c(paste("Family: Ordinal <br /> Link: ",new.model$info$link,sep=""),rep(NA,6),rep(NA,v.p.rep))
      # glance_stats[[1]]=c("Null.dev","Chi-Sq","dF","Pr(>Chisq)")
      # glance_stats[[2]]=c({{-2*my.model$logLik}-ddeviance2},ddeviance2,ddf2,pvalString(fit2, digits = 3, format = "default"))
      # glance_stats[[3]]=c("Family: ","Link: ",NA,NA)
      # glance_stats[[4]]=c("ordinal",levels(my.model$info$link),NA,NA)
      # glance_stats[[5:{5+v.p.rep}]]=c(NA,NA,NA,NA)
      # glance_stats[[{6+v.p.rep}]]=c("logLik","AIC","BIC","deviance")
      # glance_stats[[{7+v.p.rep}]]=c(my.model$logLik,as.numeric(levels(my.model$info$AIC)),BIC(my.model),{-2*my.model$logLik})
    }else{

    }
  }
  #### For total
  the.length = the.length + 1
  this.temp.var = this.temp.var + 1
  #### Make table ####

  options(pixie_interactive = pix.int,
          pixie_na_string = "")


  if (type == "lm") {
    the.length=the.length+2
    my.dust = pixiedust::dust(my.tables.df) %>%
      sprinkle(cols = "p.val", fn = quote(pvalString(
        value, digits = 3, format = "default"
      ))) %>%
      sprinkle_print_method(pix.method) %>%
      sprinkle_na_string() %>%
      sprinkle(
        rows = 1:the.length,
        cols = v.p.len,
        border = "right",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1:the.length,
        cols = 1,
        border = "left",
        border_color = "black"
      ) %>%
      sprinkle(
        rows=2,
        cols=1:v.p.len,
        border=c("top","bottom")
      )%>%
      sprinkle(rows=this.temp.var-1,
               border="top")%>%
      sprinkle(
        rows = 1,
        cols = 1:v.p.len,
        border = c("top", "bottom"),
        border_color = "black",
        part = "head"
      ) %>%
      sprinkle(rows=1,cols=1,border="left",part="head")%>%
      sprinkle(rows=1,cols=v.p.len,border="right",part="head")%>%
      sprinkle(
        rows = this.temp.var+1,
        cols = 1:v.p.len,
        border = "bottom",
        border_color = "black"
      )

    if (!VIF & !part.eta) {
      my.dust = my.dust %>%
        sprinkle(cols = c("sumsq", "est", "std.err", "f.val"),
                 round = 2) %>%
        sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
        sprinkle_colnames(
          "Variable",
          "Estimate",
          "Std. Error",
          paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
          "df",
          "F-value",
          "Pr(>F)"
        ) %>%
        sprinkle_align(rows = 1,
                       halign = "center",
                       part = "head") %>%
        sprinkle_pad(rows = 1,
                     pad = 5,
                     part = "head")

    } else if (!VIF) {
      my.dust = my.dust %>%
        sprinkle(
          cols = c("sumsq", "est", "std.err", "f.val", "p.eta"),
          round = 2
        ) %>%
        sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
        sprinkle_colnames(
          "Variable",
          "Estimate",
          "Std. Error",
          paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
          "df",
          "F-value",
          "Pr(>F)",
          "Part <br /> eta"
        ) %>%
        sprinkle_align(rows = 1,
                       halign = "center",
                       part = "head") %>%
        sprinkle_pad(rows = 1,
                     pad = 5,
                     part = "head")

    } else if (!part.eta) {
      my.dust = my.dust %>%
        sprinkle(
          cols = c("sumsq", "est", "std.err", "f.val", "VIF"),
          round = 2
        ) %>%
        sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
        sprinkle_colnames(
          "Variable",
          "Estimate",
          "Std. Error",
          paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
          "df",
          "F-value",
          "Pr(>F)",
          "VIF"
        ) %>%
        sprinkle_align(rows = 1,
                       halign = "center",
                       part = "head") %>%
        sprinkle_pad(rows = 1,
                     pad = 5,
                     part = "head")

    } else{
      my.dust = my.dust %>%
        sprinkle(
          cols = c("sumsq", "est", "std.err", "f.val", "p.eta", "VIF"),
          round = 2
        ) %>%
        sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
        sprinkle_colnames(
          "Variable",
          "Estimate",
          "Std. Error",
          paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
          "df",
          "F-value",
          "Pr(>F)",
          "Part <br /> eta",
          "VIF"
        ) %>%
        sprinkle_align(rows = 1,
                       halign = "center",
                       part = "head") %>%
        sprinkle_pad(rows = 1,
                     pad = 5,
                     part = "head")

    }

  } else if (type == "glm2") {
    my.dust = pixiedust::dust(my.tables.df) %>%
      sprinkle(cols = "p.val", fn = quote(pvalString(
        value, digits = 3, format = "default"
      ))) %>%
      sprinkle_print_method(pix.method) %>%
      sprinkle_na_string() %>%
      sprinkle(cols = 2:5, round = 2) %>%
      sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
      sprinkle(cols = 2:{7+v.p.rep},
               pad = 5,
               part = "head") %>%
      sprinkle(
        rows = {
          this.temp.var - 2
        },
        cols = 1:v.p.len,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1:the.length,
        cols = 1:v.p.len,
        border = "right",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1:the.length,
        cols = 1,
        border = "left",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1,
        cols = 1:v.p.len,
        border = c("top", "bottom", "left", "right"),
        border_color = "black",
        part = "head"
      ) %>%
      sprinkle(
        rows = this.temp.var - 1,
        cols = 1:v.p.len,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle_align(rows = 1,
                     halign = "center",
                     part = "head")

    if (!VIF) {
      my.dust = my.dust %>% sprinkle_colnames("Variable",
                                              "Odds Ratio",
                                              "Std. Error",
                                              "z-Value",
                                              "Deviance",
                                              "df",
                                              "p-Value")
    } else{
      my.dust = my.dust %>% sprinkle_colnames(
        "Variable",
        "Odds Ratio",
        "Std. Error",
        "z-Value",
        "Deviance",
        "df",
        "p-Value",
        "VIF"
      ) %>%
        sprinkle_round(cols = 8, round = 2)
    }

  } else{
    my.dust = pixiedust::dust(my.tables.df) %>%
      sprinkle(cols = "p.val", fn = quote(pvalString(
        value, digits = 3, format = "default"
      ))) %>%
      sprinkle_print_method(pix.method) %>%
      sprinkle_na_string() %>%
      sprinkle(cols = 2:5, round = 2) %>%
      sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
      sprinkle(cols = 2:7,
               pad = 5,
               part = "head") %>%
      sprinkle(
        rows = this.temp.var+1,
        cols = 1:7,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1,
        cols = 1:2,
        border = c("top", "bottom"),
        border_color = "black",
        part = "head"
      ) %>%
      sprinkle(
        rows = 1,
        cols = 3:4,
        border = c("top", "bottom"),
        border_color = "black",
        part = "head"
      ) %>%
      sprinkle(
        rows = 1,
        cols = 5:{7+v.p.rep},
        border = c("top", "bottom"),
        border_color = "black",
        part = "head"
      ) %>%
      sprinkle(
        rows = this.temp.var - 1,
        cols = 1:7,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1,
        cols = 1:7,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = this.temp.var - 2,
        cols = 1:7,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 1+total.intercepts,
        cols = 1:7,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle(
        rows = 2+total.intercepts,
        cols = 1:7,
        border = "bottom",
        border_color = "black"
      ) %>%
      sprinkle_colnames("Variable",
                        "Odds Ratio",
                        "Conf. <br /> 2.5%","Int. <br /> 97.5%",
                        "Deviance",
                        "df",
                        "Pr(>Chi)")%>%
      sprinkle_border(cols=1,border=c("left","right"))%>%
      sprinkle_border(cols={7+v.p.rep},border="right")%>%
      sprinkle_border(cols=1,border=c("left","right"),part="head")%>%
      sprinkle_border(cols={7+v.p.rep},border="right",part="head")
  }
  if(do.glance){
    if (type == "lm") {
      my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
        sprinkle_na_string(part = "foot") %>%
        sprinkle(rows=1,merge=T,halign="center",part="foot")
      # my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
      #   sprinkle(cols = c(2, {
      #     7 + v.p.rep
      #   }),
      #   round = 3,
      #   part = "foot") %>%
      #   sprinkle(cols = 3:{
      #     5 + v.p.rep
      #   },
      #   replace = c(rep("", {
      #     12 + 4 * v.p.rep
      #   })),
      #   part = "foot") %>%
      #   sprinkle(
      #     cols = 1,
      #     replace = c("R-Square", "Adj R-Sq", "F-Statistic", "P-Value"),
      #     part = "foot"
      #   ) %>%
      #   sprinkle(
      #     cols = 2,
      #     rows = 4,
      #     fn = quote(pvalString(
      #       value, digits = 3, format = "default"
      #     )),
      #     part = "foot"
      #   ) %>%
      #   sprinkle(
      #     cols = 1:v.p.len,
      #     rows = 1,
      #     halign = "center",
      #     part = "head"
      #   ) %>%
      #   sprinkle_width(cols = 1,
      #                  width = 90,
      #                  width_units = "pt") %>%
      #   sprinkle_width(cols = 2,
      #                  width = 108,
      #                  width_units = "pt") %>%
      #   sprinkle_width(cols = 4,
      #                  width = 62,
      #                  width_units = "pt") %>%
      #   sprinkle_width(cols = 5,
      #                  width = 68,
      #                  width_units = "pt") %>%
      #   sprinkle_width(cols = 6,
      #                  width = 68,
      #                  width_units = "pt") %>%
      #   sprinkle_width(cols = 7,
      #                  width = 71,
      #                  width_units = "pt") %>%
      #   sprinkle(cols = 2,
      #            halign = "left",
      #            part = "foot")
    } else{
      my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
        sprinkle_na_string(part = "foot") %>%
        sprinkle(rows=1,merge=T,halign="center",part="foot")
    }
  }
  if (pix.int) {
    return(my.dust)
  } else{
    my.dust.print = print(my.dust, quote = F)[1]
    return(my.dust.print)
  }

}